@ARTICLE{toutounji2014,
author = {Toutounji, Hazem and Pasemann, Frank},
title = {Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons},
journal = {Frontiers in Neurorobotics},
volume = {8},
year = {2014},
number = {19},
url = {http://www.frontiersin.org/neurorobotics/10.3389/fnbot.2014.00019/abstract},
doi = {10.3389/fnbot.2014.00019},
issn = {1662-5218},
abstract = {The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered, or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with eighteen degrees of freedom, and obstacle-avoidance of a wheel-driven robot.},
pubtype = {other}
}

@INCOLLECTION{rempis2013,
author = {Christian W. Rempis},
title = {A Neural Network to Capture Demonstrated Motions on a Humanoid Robot to Rapidly Create Complex Central Pattern Generators As Reusable Neural Building Blocks},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA 2013)},
pages = {},
year = {2013},
publisher = {IEEE},
abstract = {Many neurorobotics experiments require central pattern generators (CPGs) and motion primitives for the target robot, which have to be given prior to an experiment as neural building blocks. The creation of artificial neural networks that produce the desired motions is a tedious and time-consuming task. Furthermore, rapidly varying motion pattern to test alternative motions is difficult due to the rigid nature of the usually hard-wired networks. To overcome this problem, we introduce a novel network architecture that allows to capture motions directly from demonstrated movements on the robot hardware. The demonstrated motion pattern are stored in the activation dynamics of the network, instead of as synaptic weights. This allows the rapid creation and modification of neural CPGs for various, even complex motions directly on the robot. With a simple adaptation method, the activation dynamics representing the captured motions can also be used to determine synaptic weights to create fixed, reusable neural building blocks. To demonstrate the applicability of the proposed neural network architecture we generate two arm motions for a humanoid robot by demonstration. The successful applications show that the neural motion capturing approach is a useful method to generate CPGs for neurorobotics experiments.},
pubtype = {conference}
}

@MISC{Rempis2013Modulator,
author = {Christian Rempis and Hazem Toutounji and Frank Pasemann},
title = {Controlling the Learning of Behaviors in the Sensorimotor Loop with
Neuromodulators in Self-Monitoring Neural Networks},
year = {2013},
abstract = {Using neuronal plasticity in the sensorimotor loop of embodied controllers
to autonomously learn behaviors remains a great challenge. The difficulty
lies not only in the development of sophisticated plasticity mechanisms,
but also in controlling when, where and how to learn, in order to
achieve the correct behavior. Borrowing from biology, we develop
a general framework that deploys intrinsic biofeedback-like signals
to assess the behavior through self-monitoring and to control learning
accordingly. The framework augments a plastic control network (CSN)
with a static neuromodulator (NM) subnetwork (MSN), containing cells
capable of producing NM as feedback signal in response to observed
network activations. A desired behavior can then be defined solely
by specifying the conditions for NM release. As a response to NM
exposure, the CSN changes until a new stable configuration without
NM release is reached, i.e. the desired behavior is performed. We
demonstrate this approach with experiments in which simple behaviors
are learned from scratch. We show how the behaviors can be defined
with MSNs and that - in combination with a simple stochastic plasticity
method - behaviors are successfully learned.},
howpublished = {ICRA Workshop on Autonomous Learning},
pubtype = {conference},
pdf-web = {http://autonomous-learning.org/wp-content/uploads/13-ALW/paper_9.pdf}
}

@PHDTHESIS{zahedi08phd,
author = {Keyan Mahmoud Ghazi-Zahedi},
title = {Self-Regulating Neurons. {A} model for synaptic plasticity in artificial
recurrent neural networks},
school = {University of Osnabr\"uck},
year = {2008},
abstract = {Robustness and adaptivity are important behavioural properties observed
in biological systems, which are still widely absent in articial
intelligence applications. Such static or non-plastic articial systems
are limited to their very specic problem domain. This work introducesa
general model for synaptic plasticity in embedded articial recurrent
neural networks, which is related to short-term plasticity by synaptic
scaling in biological systems. The model is general in the sense
that is does not require trigger mechanisms or articial limitations
and it operates on recurrent neural networks of arbitrary structure.
A Self-Regulation Neuron is dened as a homeostatic unit which regulates
its activity against external disturbances towards a target value
by modulation of its incoming and outgoing synapses. Embedded and
situated in the sensori-motor loop, a network of these neurons is
permanently driven by external stimuli andwill generally not settle
at its asymptotically stable state. The system´s behaviour is determinedby
the local interactions of the Self-Regulating Neurons.
The neuron model is analysed as a dynamical system with respect to
its attractor landscape and its transient dynamics. The latter is
conducted based on dierent control structures for obstacle avoidance
with increasing structural complexity derived from literature. The
result isa controller that shows rst traces of adaptivity. Next,
two controllers for dierent tasks are evolved and their transient
dynamics are fully analysed.
The results of this work not only show that the proposed neuron model
enhances the behavioural properties, but also points out the limitations
of short-term plasticity which does not account for learning and
memory.},
file = {zahedi08phd.pdf:zahedi08phd.pdf:PDF;zahedi08phd.pdf:zahedi08phd.pdf:PDF},
keywords = {Recurrent Neural Network, Evolutionary Robotics, Homeostasis},
timestamp = {2009.11.25}
}

@INPROCEEDINGS{Huelse2005,
author = {Martin H\"ulse and Steffen Wischmann and Frank Pasemann},
title = {The role of non-linearity for evolved multifunctional robot behavior},
booktitle = {ICES 2005, Evolvable systems: From biology to hardware},
year = {2005},
editor = {J. M. Moreno and J. Madrenas and J. Cosp},
number = {3637},
series = {LNCS},
pages = {108--118},
abstract = {In this paper the role of non-linear control structures for the development
of multifunctional robot behavior in a self-organized way is discussed.
This discussion is based on experiments where combinations of two
behavioral tasks are incrementally evolved. The evolutionary experiments
develop recurrent neural networks of general type in a systematically
way. The resulting networks are investigated according to the underlying
structure-function relations. These investigations point to necessary
properties providing multifunctionality, scalability, and open-ended
evolutionary strategies in Evolutionary Robotics.},
pdf-web = {http://www.ikw.uni-osnabrueck.de/~neurokybernetik/media/pdf/2005-2.pdf},
pubtype = {conference},
timestamp = {2007.03.28}
}

@ARTICLE{Huelse2004a,
author = {Martin H\"ulse and Steffen Wischmann and Frank Pasemann},
title = {Structure and function of evolved neuro-controllers for autonomous
robots},
journal = {Connection Science},
year = {2004},
volume = {16},
pages = {249--266},
number = {4},
abstract = {The artificial life approach to evolutionary robotics is used as a
fundamental framework for the development of a modular neural control
of autonomous mobile robots. The applied evolutionary technique is
especially designed to grow different neural structures with complex
dynamical properties. This is due to a modular neurodynamics approach
to cognitive systems, stating that cognitive processes are the result
of interacting dynamical neuro-modules. The evolutionary algorithm
is described, and a few examples for the versatility of the procedures
are given. Besides solutions for standard tasks like exploration,
obstacle avoidance and tropism, also the sequential evolution of
morphology and control of a biped is demonstrated. A further example
describes the co-evolution of different neuro-controllers co-operating
to keep a gravitationally driven art-robot in constant rotation.},
keywords = {modular neuro-dynamics, nonlinear robot control, perception-action
systems, structure evolution, evolution of morphology},
pub-web = {http://www.ingentaconnect.com/content/tandf/ccos/2004/00000016/00000004/art00003},
pubtype = {refereed},
timestamp = {2007.03.28}
}

@INPROCEEDINGS{Klaassen2004a,
author = {Bernhard Klaassen and Keyan Zahedi and Frank Pasemann},
title = {A {Mo}dular {A}pproach to {C}onstruction and {C}ontrol of {W}alking
{R}obots},
booktitle = {Robotik 2004: Leistungsstand, Anwendungen, Visionen, Trends},
year = {2004},
number = {1841},
series = {VDI-Berichte},
pages = {633--640},
abstract = {In our view a walking machine is not a goal in itself. Of course,
on one hand there are interesting applications, especially for exploration
tasks, but on the other hand, a walking robot serves as demonstrator
for nonlinear and adaptive control tasks with a high number of degrees-of-freedom
and fast-changing sensor inputs. Here we describe a modular approach,
not only for the construction, but also for control aspects: The
control technique is called Pose Fitting Networks (PFN) and is able
to adapt a basic set of small, recurrent neural networks to a user-defined
sequence of robot poses, such that the output nodes of the net can
drive the robot's legs periodically through the sequence of poses.},
pdf-web = {http://www.ikw.uni-osnabrueck.de/~neurokybernetik/media/pdf/2004-2.pdf},
pubtype = {conference},
timestamp = {2007.03.28}
}

@INPROCEEDINGS{Manoonpong2005a,
author = {Poramate Manoonpong and Frank Pasemann and J\"orn Fischer},
title = {Modular neural control for a reactive behavior of walking machines},
booktitle = {Proceedings of the 6th IEEE Symposium on Computational Intelligence
in Robotics and Automation, (CIRA2005)},
year = {2005},
pages = {403--408},
address = {Helsinki University of Technology, Finland},
note = {27 - 30 June},
abstract = {A small modular neural network is presented which is able to control
the sensor-driven behavior of walking machines with many degrees
of freedom. The controller is composed of a so called minimal recurrent
controller (MRC) for sensory signal processing, a SO(2)-network as
neural oscillator to generate the rhythmic leg movements, and a velocity
regulating network (VRN) which expands the steering capabilities
of the walking machine. This recurrent neurocontroller enables the
machine to explore an in-door environment by avoiding obstacles.
It was developed and tested using a physical simulation environment,
and was then successfully transferred to the physical four-legged
walking machine, called AMOS-WD02.},
isbn = {0-7803-9355-4},
pdf-web = {http://www.ikw.uni-osnabrueck.de/~neurokybernetik/media/pdf/2005-3.pdf},
pubtype = {conference},
timestamp = {2007.03.28}
}

@INPROCEEDINGS{Manoonpong2004,
author = {Poramate Manoonpong and Frank Pasemann and J\"orn Fischer},
title = {Neural {P}rocessing of {A}uditory-tactile {S}ensor {D}ata to {P}erform
{R}eactive {B}ehavior of {W}alking {M}achines },
booktitle = {Proceedings of the IEEE MechRob2004. CD-ROM},
year = {2004},
pages = {189--194},
abstract = {Spiders can sense sounds in a frequency range between approximately
40 and 600 Hz by the use of hairs; they can detect e.g. the puff
of wind of buzzing flies. On the contrary, scorpions use hairs as
tactile sensors for obstacle avoidance. To integrate the advantages
of both types of sensoric hairs, this article presents an artificial
auditory-tactile sensor system, which combines the principles of
the auditory hairs of spiders and the tactile hairs of scorpions,
and investigates some neural techniques for processing these sensor
signals. The different types of signals are discerned by recurrent
neural networks in such a way that their output can generate different
reactive behavior, like obstacle avoidance and tropism, of a walking
machine. An evolutionary algorithm is applied to find an appropriate
solution to this problem.},
pdf-web = {http://www.ikw.uni-osnabrueck.de/~neurokybernetik/media/pdf/2004-6.pdf},
pubtype = {conference},
timestamp = {2007.03.28}
}

@ARTICLE{Manoonpong2005b,
author = {Poramate Manoonpong and Frank Pasemann and J\"orn Fischer and Hubert
Roth},
title = {Neural {P}rocessing of {A}uditory {S}ignals and {M}odular {N}eural
{C}ontrol for {S}ound {T}ropism of {W}alking {M}achines},
journal = {International Journal of Advanced Robotic Systems},
year = {2005},
volume = {2},
pages = {223--235},
number = {2},
abstract = {The specialized hairs and slit sensillae of spiders (Cupiennius salei)
can sense the airflow and auditory signals in a low-frequency range.
They provide the sensor information for reactive behavior, like e.g.
capturing a prey. In analogy, in this paper a setup is described
where two microphones and a neural preprocessing system together
with a modular neural controller are used to generate a sound tropism
of a four-legged walking machine. The neural preprocessing network
is acting as a low-pass filter and it is followed by a network which
discerns between signals coming from the left or the right. The parameters
of these networks are optimized by an evolutionary algorithm. In
addition, a simple modular neural controller then generates the desired
different walking patterns such that the machine walks straight,
then turns towards a switched-on sound source, and then stops near
to it.},
issn = {1729-5506},
keywords = {recurrent neural networks, neural control, auditory signal processing,
autonomous robots, walking machines},
pdf-web = {http://intechweb.org/downloadpdf.php?id=4136},
pubtype = {refereed},
timestamp = {2007.03.28}
}

@MASTERSTHESIS{twickel04,
author = {Arndt von Twickel},
title = {Obstacle perception by scorpions and robots -- {F}rom biology to
robotics via physical simulation and evolving neural networks},
school = {Universit\"at Bonn},
year = {2004},
type = {{Diplomarbeit}},
abstract = {Locomotion has not been understood well enough to build robots that
autonomously navigate through rough terrain. The current understanding
of locomotion implies a highly decentralized and modular control
structure. Two experimental approaches, each addressing a different
level of control, have been made to gain insight into the mechanisms
of obstacle perception as an integral part of rough terrain locomotion.
In the first approach controllers were developed for single, morphological
distinct legs through artificial evolution and physical simulation.
The results showed re ex-oscillators which inherently relied on the
sensori-motor loop and a hysteresis effect. Successful coupling of
six controllers, exclusively by means of the sensori-motor loop,
showed the applicability of the modular concept. In a second approach
a behavioural experiment was conducted with scorpions (Pandinus Cavimanus
(POCOCK)) walking on a locomotion compensator and making contact
with obstacles of different heights. The experiment showed that the
scorpions employed their pedipalps (especially the obstacle facing
one) for rhythmic groping movements. No coupling of the pedipalp
rhythm to the leg movement could be found. Further prolonged phases
of exclusive hair-contact and \hair-brushing" behaviours have been
observed, suggesting an important role of the pedipalps and their
hairs in the process of obstacle detection and therefore in rough
terrain locomotion. Taken together, the results establish a basis
for future integration of the two approaches.},
file = {twickel04.pdf:/home/arndt/daten/doktorarbeit/literatur/papers/twickel04.pdf:PDF},
groups = {indy},
keywords = {indy},
pdf-web = {http://www.ikw.uni-osnabrueck.de/~neurokybernetik/media/pdf/twickel04.pdf},
pubtype = {thesis},
timestamp = {2006.12.12}
}

@INPROCEEDINGS{twickel06c,
author = {Arndt von Twickel and Manfred Hild and Torsten Siedel and Frank Pasemann},
title = {Octavio: Autonomous legs for a reconfigurable walking machine},
booktitle = {HLR 2006, French-German Workshop on Humanoid and Legged Robots},
year = {2006},
editor = {Christian Simonidis},
address = {Karlsruhe},
month = {September},
abstract = {A modular approach, in software as well as in hardware, to the development
of a robust, compliant and sensordriven walking machine is taken.
The walking
machine Octavio consists of single, energy- and control-autonomous
legs with 3 Degrees of Freedom. Spring couplings and pre-stressed
springs in the joints make the legs robust and allow for a very high
payload when compared with most other walking machines. The walking
machine is easily and rapidly reconfigurable, e.g. to a four-, six-
or eight-legged one, by connecting multiple legs to diversly shaped
torsi which provide communication pathways between the autonomous
legs rather than serving as a central control unit. Consistent with
the nature of the hardware, modular and distributed controllers are
developed. Initially controllers are developed for single legs to
later couple multiple single leg controllers to drive the whole walking
machine. Controllers consist of recurrent neural networks that are
developed and optimized by artificial evolution. This approach inherently
promotes the inclusion of the sensorimotor-loop into the controllers.
The goal of this endeavor is to build up a library of efficient control
mechanisms that enable the walking machine to show a robust performance
under rough terrain conditions and to compare these mechanisms with
other technical control strategies and with recently discovered neural
mechanisms of walking control in animals.},
conf-web = {http://www.itm.uni-karlsruhe.de/hlr2006/},
file = {twickel06c.pdf:twickel06c.pdf:PDF},
pubtype = {conference},
timestamp = {2006.12.12}
}

@INPROCEEDINGS{Wischmann2005,
author = {Steffen Wischmann and Martin H\"ulse and Frank Pasemann},
title = {(Co){E}volution of ({D}e)centralized {N}eural {C}ontrol for a {G}ravitationally
{D}riven},
booktitle = {ECAL 2005},
year = {2005},
editor = {Machine.Capcarrere et al.},
volume = {3630},
pages = {179--188},
publisher = {LNAI},
abstract = {Using decentralized control structures for robot control can offer
a lot of advantages, such as less complexity, better fault tolerance
and more flexibility. In this paper the evolution of recurrent artificial
neural networks as centralized and decentralized control architectures
will be demonstrated. Both designs will be analyzed concerning their
structure-function relations and robustness against lesion experiments.
As an application, a gravitationally driven robotic system will be
introduced. Its task can be allocated to a cooperative behavior of
five subsystems. A co-evolutionary strategy for generating five autonomous
agents in parallel will be described.},
pdf-web = {http://www.ikw.uni-osnabrueck.de/~neurokybernetik/media/pdf/2005-8.pdf},
pubtype = {conference},
timestamp = {2007.03.28}
}